What tweets got the most attention. frequency of tweets posted on a topic

library(rtweet)
library(ggplot2)
library(tidyr)
library(dplyr)

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library(tidytext)
library(stringr)
library(scales)
library(readr)

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library(lubridate)

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library(vader)
library(topicmodels)
library(quanteda)
Package version: 3.0.0
Unicode version: 13.0
ICU version: 69.1
Parallel computing: 8 of 8 threads used.
See https://quanteda.io for tutorials and examples.
library(lubridate)
library(zoo)

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library(plotly)
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library(forcats)
library(igraph)

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library(ggraph)
library(widyr)
library(qdapRegex)

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    %+%
origop <- options("httr_oauth_cache")
options(httr_oauth_cache = TRUE)

api_key <- "5PgtS7ljq5ZbBoXnemU5qHe62"
api_secret <- "M44LeduQ4zoyDxQIkAFjeIJrpDhWnb5xASDvhahTlrAvOhN7fx"
access_token <- "743029724750942208-JLEp26XrjwvQ1CPJUXwvdUMLka82cgx"
access_secret <- "XRMeMBaOgQy2BC1Bd9iJARfMIyK40VKyII1ZRcf9nS0qd"
token <- create_token(
  app = "KyleResearchApp",
  consumer_key = api_key,
  consumer_secret = api_secret,
  access_token = access_token,
  access_secret = access_secret
)
media_agency_df <- read_csv("data_in/media_agency_tweets.csv")

-- Column specification -----------------------------------------------------------------------------------------------------------
cols(
  .default = col_character(),
  created_at = col_datetime(format = ""),
  display_text_width = col_double(),
  reply_to_status_id = col_logical(),
  reply_to_user_id = col_logical(),
  reply_to_screen_name = col_logical(),
  is_quote = col_logical(),
  is_retweet = col_logical(),
  favorite_count = col_double(),
  retweet_count = col_double(),
  quote_count = col_logical(),
  reply_count = col_logical(),
  symbols = col_logical(),
  ext_media_type = col_logical(),
  quoted_status_id = col_logical(),
  quoted_text = col_logical(),
  quoted_created_at = col_logical(),
  quoted_source = col_logical(),
  quoted_favorite_count = col_logical(),
  quoted_retweet_count = col_logical(),
  quoted_user_id = col_logical()
  # ... with 30 more columns
)
i Use `spec()` for the full column specifications.

207 parsing failures.
 row                  col           expected               actual                              file
2282 reply_to_status_id   1/0/T/F/TRUE/FALSE x1405793506459783168 'data_in/media_agency_tweets.csv'
2282 reply_to_user_id     1/0/T/F/TRUE/FALSE x1358665806121467908 'data_in/media_agency_tweets.csv'
2282 reply_to_screen_name 1/0/T/F/TRUE/FALSE LA89db               'data_in/media_agency_tweets.csv'
3687 reply_to_user_id     1/0/T/F/TRUE/FALSE x240546747           'data_in/media_agency_tweets.csv'
3687 reply_to_screen_name 1/0/T/F/TRUE/FALSE MasegoRahlaga        'data_in/media_agency_tweets.csv'
.... .................... .................. .................... .................................
See problems(...) for more details.

Comparing Tweet Archives

Some tweets fetched date further back. The 3200 tweet pull per user causes this. It tells us that agencies like News24 and SABC News Tweet more daily than Daily Maverick.

ggplot(media_agency_df, aes(x = created_at, fill = screen_name)) +
  geom_histogram(position = "identity", bins = 20, show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 1)

We first clean the original text to remove links, punctuation, digits, links, @’s. Then we tokenize the Tweets and remove stop words from tidytext and our own stopword dictionary. We also use the “twitter” token to handle any left over @’s and URLS.

#tidy df and unnest
tidy_media_df <- media_agency_df %>% 
  #filter(str_detect(text, fixed(covid_dictionary, ignore_case = TRUE))) %>%
  unnest_tokens(word, text, token = "tweets") %>% 
  filter(!word %in% stop_words$word,
         !word %in% agency_stop_words$word,
        # !word %in% negated_words$word2,
         !word %in% str_remove_all(stop_words$word, "'"),
         str_detect(word, "[a-z]"))
Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.

From our tidyed Tweet dataset, we look for the top words that appear. This will give us a good idea of what topics are being discussed the most. We find that COVID-19 has been the main topic of discussion. President appears second as President Ramaphosa of South Africa usually adresses the nation regarding COVID-19 information. Additionally, Zuma also appears as he is mentioned as “former president Zuma” in most articles. Zuma appears more as his recent court avoidance and sentencing is being Tweeted. General words surrounding the COVID-19 topic as it is still the main pressure on the country, espicially involving Gauteng’s rise in infections.

#top words
top_words <- tidy_media_df %>% 
  anti_join(stop_words) %>% 
  count(word) %>% 
  arrange(desc(n))
top_words %>%
  slice(1:20) %>%
  ggplot(aes(reorder(word, -n), n, fill = word)) +
  geom_bar(stat="identity") +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 60, hjust = 1, size = 13),
    plot.title = element_text(hjust = 0.5, size = 18)
    ) +
  ylab("Frequency") +
  xlab ("") +
  ggtitle("Most frequent media agency tweets") +
  guides(fill=FALSE)

News

#tf-idf
tidy_media_tf_idf <- tidy_media_df %>% 
  select(screen_name, word) %>% 
  count(word, screen_name) %>% 
  bind_tf_idf(word, screen_name, n) 

#calculate a frequency for each agency and word
frequency <- tidy_media_tf_idf %>% 
  group_by(screen_name) %>% 
  count(word, sort = TRUE) %>% 
  left_join(tidy_media_df %>% 
              group_by(screen_name) %>% 
              summarise(total = n())) %>%
  mutate(freq = n/total)
Joining, by = "screen_name"
tidy_media_tf_idf %>% filter(word == "iss")
tidy_media_df %>% filter(word == "iss")

tidy_media_tf_idf %>%
  group_by(screen_name) %>%
  slice_max(tf_idf, n = 15) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = screen_name)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 2, scales = "free") +
  labs(x = "tf-idf", y = NULL)

By modelling bigrams and trigrams for our dataset gives us a better understanding of what topic is being discussed with each word. We can then also see which sentiments are incorrectly labeled. “not good” gives better context of a negative sentiment, rather than it being incorrectly identified as positive good.

We give more weight to words that appear more often with the incorrect sentiment. We can now reverse the sentiment of these words once VADER has been run on the dataset.

Trigrams were modelled but were not necessary as bigrams provided enough information

#trigrams
tidy_trigram_df <- media_agency_df %>%
  filter(!str_detect(text, "^RT")) %>%
  mutate(text = gsub(" ?(f|ht)tp(s?)://(.*)[.][a-z]+", " ", text)) %>%
  unnest_tokens(trigram, text, token = "ngrams", n = 3) %>%
  separate(trigram, c("word1", "word2", "word3"), sep = " ")

trigrams_filtered <- trigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>% 
  filter(!word3 %in% stop_words$word) 

# new trigrams counts:
trigram_counts <- trigrams_filtered %>% 
  count(word1, word2, word3, sort = TRUE)

trigrams_united <- trigrams_filtered %>%
  unite(trigram, word1, word2, word3, sep = " ")

#tf-idf trigrams
tidy_trigram_df <- trigrams_united %>%
  count(screen_name, trigram) %>%
  bind_tf_idf(trigram, screen_name, n) %>%
  arrange(desc(tf_idf))

tidy_trigram_df %>%
  group_by(screen_name) %>%
  slice_max(tf_idf, n = 10) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(trigram, tf_idf), fill = screen_name)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 2, scales = "free") +
  labs(x = "tf-idf", y = NULL)

#sentiment
ggplotly(ggplot(data=media_vader_df, aes(x=created_at)) +
  geom_line(aes(y=rollmean(compound, k=30, na.pad = TRUE)), color="pink", size=.5)+
  geom_smooth(aes(y=compound)) +
  theme_minimal()+
  facet_wrap(~screen_name) +
  scale_y_continuous(expand = c(0,0)))
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Removed 979 rows containing non-finite values (stat_smooth).

install.packages("ldatuning")
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    Arun2010                                html  
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    CaoJuan2009                             html  
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    Deveaud2014                             html  
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    FindTopicsNumber                        html  
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    Griffiths2004                           html  
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library("ldatuning")


library("topicmodels")
data("AssociatedPress", package="topicmodels")
dtm <- AssociatedPress[1:10, ]


result <- FindTopicsNumber(
  tidy_matrix,
  topics = seq(from = 2, to = 15, by = 1),
  metrics = c("Griffiths2004", "CaoJuan2009", "Arun2010", "Deveaud2014"),
  method = "Gibbs",
  control = list(seed = 77),
  mc.cores = 2L,
  verbose = TRUE
)
fit models... done.
calculate metrics:
  Griffiths2004... done.
  CaoJuan2009... done.
  Arun2010... done.
  Deveaud2014... done.
FindTopicsNumber_plot(result)
`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.

---
title: "R Notebook"
output: html_notebook
---

What tweets got the most attention.
frequency of tweets posted on a topic


```{r SETUP}
library(rtweet)
library(ggplot2)
library(tidyr)
library(dplyr)
library(tidytext)
library(stringr)
library(scales)
library(readr)
library(lubridate)
library(vader)
library(topicmodels)
library(quanteda)
library(lubridate)
library(zoo)
library(plotly)
library(forcats)
library(igraph)
library(ggraph)
library(widyr)
library(qdapRegex)

origop <- options("httr_oauth_cache")
options(httr_oauth_cache = TRUE)

api_key <- "5PgtS7ljq5ZbBoXnemU5qHe62"
api_secret <- "M44LeduQ4zoyDxQIkAFjeIJrpDhWnb5xASDvhahTlrAvOhN7fx"
access_token <- "743029724750942208-JLEp26XrjwvQ1CPJUXwvdUMLka82cgx"
access_secret <- "XRMeMBaOgQy2BC1Bd9iJARfMIyK40VKyII1ZRcf9nS0qd"
token <- create_token(
  app = "KyleResearchApp",
  consumer_key = api_key,
  consumer_secret = api_secret,
  access_token = access_token,
  access_secret = access_secret
)
```

```{r Fetching Tweets}
media_agency_df <- get_timeline("News24", n = 3200)
media_agency_df <- media_agency_df %>% 
  bind_rows(get_timeline("eNCA", n = 3200)) %>% 
  bind_rows(get_timeline("TimesLIVE", n = 3200)) %>% 
  bind_rows(get_timeline("SABCNews", n = 3200)) %>% 
  bind_rows(get_timeline("dailymaverick", n = 3200))

write_as_csv(media_agency_df, "data_in/media_agency_tweets")
media_agency_df <- read_csv("data_in/media_agency_tweets.csv")

media_agency_df <- media_agency_df[ , colSums(is.na(media_agency_df)) < nrow(media_agency_df)]
```


# Comparing Tweet Archives

Some tweets fetched date further back. The 3200 tweet pull per user causes this. It tells us that agencies like News24 and SABC News Tweet more daily than Daily Maverick.
```{r}
ggplot(media_agency_df, aes(x = created_at, fill = screen_name)) +
  geom_histogram(position = "identity", bins = 20, show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 1)
```

We first clean the original text to remove links, punctuation, digits, links, @'s.
Then we tokenize the Tweets and remove stop words from tidytext and our own stopword dictionary. We also use the "twitter" token to handle any left over @'s and URLS.
```{r Clean up}

# covid_dictionary <- c("herd", "immunity", "incubation", "job", "loss", "Kits", "lockdown", "mask", "N95", "outbreak", "pandemic", "quarantine", "recovery", "sanitiser", "transmission", "Underlying", "conditions", "Ventilators", "WHO", "xenophobia", "youTube", "zoonotic", "stay-at-home", "covid", "coronavirus", "hyrdoxychloroquine", "asymptomatic", "frontline", "virus", "self-isolation", "disinfectant", "shelter-in-place", "masks", "SARS-CoV-2", "ICU", "corona", "reopen", "distancing", "covering", "furlough", "tracer", "easing", "remdesivir", "mail-in", "hornet", "antibody", "in-person", "defund", "racism", "looting", "loot", "reopen", "two-metre", "pandemic", "looter", "distancing", "dexamethasone", "racial", "vaccine", "curfew","johnssons", "astrazeneca", "hospitals", "social-distance", "social-distancing", "police", "regulations", "symptoms", "testing", "positive-tests", "negative-tests" , "confirmed-cases", "restrictions", "deaths", "infected", "recoveries", "level", "jobs", "unemployed", "doctors", "infections", "sanitise", "sanitiser", "sanitisation", "containment")

#JUST tidy
rm_twitter_n_url <- rm_(pattern=pastex("@rm_twitter_url", "@rm_url"))
media_agency_df <- media_agency_df %>% 
  mutate(text = rm_twitter_n_url(text),
         text = gsub("@\\w+", " ", text),
         text = gsub("[[:punct:]]", " ", text),
         text = gsub("[[:digit:]]", " ", text),
         text = gsub("[ \t]{2,}", " ", text),
         text = gsub("^\\s+|\\s+$", " ", text),
         text = gsub("^\\s+|\\s+$", " ", text),
         text = gsub("&amp", " ", text)) %>% 
  mutate(text = str_replace_all(text," "," "),
         text = str_replace_all(text,"RT @[a-z,A-Z]*: "," "),
         text = str_replace_all(text,"#[a-z,A-Z]*"," "),
         text = str_replace_all(text,"@[a-z,A-Z]*"," "))  
#  filter(str_detect(text, fixed(covid_dictionary, ignore_case = TRUE))) 

#most stop words are filtered based on the media agencies tag at the beginning of each Tweet. eg. WATCH: *headline follows*.
agency_stop_words <- tibble(word = c("sabcnews", "enca", "dstv", "sabckzn", "maverick", "opinionista", "dm", "scorpio", "dstv403", "itus", "rt", "amp", "tgifood", "mamelodi", "sundowns", "ofmagazineavailable", "casablanca", "oped", "newsdeck", "editorial", "newflash", "southafricanmorning", "newslink", "encas", "southafricatonight", "themiddayview", "thelead", "propertymatters", "ba", "ka", "ya", "ga", "wa", "le", "kwa", "morninglivesabc", "monday", "prix", "azerbaijan", "encasis", "encabusiness", "encasspeaks", "south", "africa", "pm", "sa","pm", "encas", "iss", "icymi", "timeslive", "fullview", "newsbreaksjul", "newsbreakjul"))  

#tidy df and unnest
tidy_media_df <- media_agency_df %>% 
  #filter(str_detect(text, fixed(covid_dictionary, ignore_case = TRUE))) %>%
  unnest_tokens(word, text, token = "tweets") %>% 
  filter(!word %in% stop_words$word,
         !word %in% agency_stop_words$word,
        # !word %in% negated_words$word2,
         !word %in% str_remove_all(stop_words$word, "'"),
         str_detect(word, "[a-z]"))

```

From our tidyed Tweet dataset, we look for the top words that appear. This will give us a good idea of what topics are being discussed the most. We find that COVID-19 has been the main topic of discussion. President appears second as President Ramaphosa of South Africa usually adresses the nation regarding COVID-19 information. Additionally, Zuma also appears as he is mentioned as "former president Zuma" in most articles. Zuma appears more as his recent court avoidance and sentencing is being Tweeted. General words surrounding the COVID-19 topic as it is still the main pressure on the country, espicially involving Gauteng's rise in infections. 
```{r top words}
#top words
top_words <- tidy_media_df %>% 
  anti_join(stop_words) %>% 
  count(word) %>% 
  arrange(desc(n))
top_words %>%
  slice(1:20) %>%
  ggplot(aes(reorder(word, -n), n, fill = word)) +
  geom_bar(stat="identity") +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 60, hjust = 1, size = 13),
    plot.title = element_text(hjust = 0.5, size = 18)
    ) +
  ylab("Frequency") +
  xlab ("") +
  ggtitle("Most frequent media agency tweets") +
  guides(fill=FALSE)
```

News
```{r tf-idf frequency per agency}
#tf-idf
tidy_media_tf_idf <- tidy_media_df %>% 
  select(screen_name, word) %>% 
  count(word, screen_name) %>% 
  bind_tf_idf(word, screen_name, n) 

#calculate a frequency for each agency and word
frequency <- tidy_media_tf_idf %>% 
  group_by(screen_name) %>% 
  count(word, sort = TRUE) %>% 
  left_join(tidy_media_df %>% 
              group_by(screen_name) %>% 
              summarise(total = n())) %>%
  mutate(freq = n/total)
tidy_media_tf_idf %>% filter(word == "iss")
tidy_media_df %>% filter(word == "iss")

tidy_media_tf_idf %>%
  group_by(screen_name) %>%
  slice_max(tf_idf, n = 15) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = screen_name)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 2, scales = "free") +
  labs(x = "tf-idf", y = NULL)
```

By modelling bigrams and trigrams for our dataset gives us a better understanding of what topic is being discussed with each word. We can then also see which sentiments are incorrectly labeled. "not good" gives better context of a negative sentiment, rather than it being incorrectly identified as positive good.
```{r bigrams}
#bigrams
tidy_bigram_df <- media_agency_df %>% 
  #filter(str_detect(text, fixed(covid_dictionary, ignore_case = TRUE))) %>%
  filter(!str_detect(text, "^RT")) %>%
  mutate(text = gsub(" ?(f|ht)tp(s?)://(.*)[.][a-z]+", " ", text)) %>%
  unnest_tokens(bigram, text, token = "ngrams", n = 2) %>% 
  separate(bigram, c("word1", "word2"), sep = " ")

bigrams_filtered <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

# new bigram counts:
bigram_counts <- bigrams_filtered %>% 
  count(word1, word2, sort = TRUE)

bigrams_united <- bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")

#tf-idf bigrams
bigram_tf_idf <- bigrams_united %>%
  count(screen_name, bigram) %>%
  bind_tf_idf(bigram, screen_name, n) %>%
  arrange(desc(tf_idf))

bigram_tf_idf %>%
  group_by(screen_name) %>%
  slice_max(tf_idf, n = 15) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(bigram, tf_idf), fill = screen_name)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 2, scales = "free") +
  labs(x = "tf-idf", y = NULL)

#weights and graphs
bigram_graph <- bigram_counts %>%
  filter(n > 100) %>%
  graph_from_data_frame()

set.seed(1234)
a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
ggraph(bigram_graph, layout = "fr") +
  geom_edge_link(aes(edge_alpha = n), show.legend = FALSE,
                 arrow = a, end_cap = circle(.07, 'inches')) +
  geom_node_point(color = "lightblue", size = 5) +
  geom_node_text(aes(label = name), vjust = 1, hjust = 1, repel = TRUE) +
  theme_void()
```

We give more weight to words that appear more often with the incorrect sentiment. We can now reverse the sentiment of these words once VADER has been run on the dataset.
```{r negation words}
#vader lexicon imported from VADER GitHub 
vader_lexicon <- read_csv2("data_in/vader_lexicon.csv") %>% 
  rename("word" = TOKEN, "value" = `MEAN-SENTIMENT-RATING`)

#common negation words
negation_words <- c("not", "no", "never", "without", "no", "not", "none", "no one", "nobody", "nothing", "neither", "nowhere", "never", "doesn’t", "isn’t", "wasn’t", "shouldn’t", "wouldn’t", "couldn’t", "won’t", "can’t", "don’t")
negated_words <- bigrams_separated %>%
  filter(word1 %in% fixed(negation_words, ignore_case = TRUE)) %>%
  inner_join(vader_lexicon, by = c(word2 = "word")) %>%
  mutate(value = as.double(value)) %>% 
  count(word1, word2, value, sort = TRUE) %>% 
  mutate(contribution = n * value) %>%
  arrange(desc(abs(contribution))) %>%
  mutate(word2 = reorder(word2, contribution))
negated_words %>%
  head(40) %>% 
  ggplot(aes(n * value, word2, fill = n * value > 0)) +
  geom_col(show.legend = FALSE) + 
  facet_wrap(~word1,scales = "free_y") +
  labs(x = "Sentiment value * # number of occurrences",
       y = "Words preceded by negation terms")

```

Trigrams were modelled but were not necessary as bigrams provided enough information
```{r trigrams}
#trigrams
tidy_trigram_df <- media_agency_df %>%
  filter(!str_detect(text, "^RT")) %>%
  mutate(text = gsub(" ?(f|ht)tp(s?)://(.*)[.][a-z]+", " ", text)) %>%
  unnest_tokens(trigram, text, token = "ngrams", n = 3) %>%
  separate(trigram, c("word1", "word2", "word3"), sep = " ")

trigrams_filtered <- trigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>% 
  filter(!word3 %in% stop_words$word) 

# new trigrams counts:
trigram_counts <- trigrams_filtered %>% 
  count(word1, word2, word3, sort = TRUE)

trigrams_united <- trigrams_filtered %>%
  unite(trigram, word1, word2, word3, sep = " ")

#tf-idf trigrams
tidy_trigram_df <- trigrams_united %>%
  count(screen_name, trigram) %>%
  bind_tf_idf(trigram, screen_name, n) %>%
  arrange(desc(tf_idf))

tidy_trigram_df %>%
  group_by(screen_name) %>%
  slice_max(tf_idf, n = 10) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(trigram, tf_idf), fill = screen_name)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~screen_name, ncol = 2, scales = "free") +
  labs(x = "tf-idf", y = NULL)

```


```{r Counting and correlating among sections, warning=FALSE}
media_agency_section_words <- media_agency_df %>%
  mutate(section = row_number() %/% 10) %>%
  filter(section > 0) %>%
  unnest_tokens(word, text) %>%
  filter(!word %in% stop_words$word)

# count words co-occuring within sections
word_pairs <- media_agency_section_words %>%
  pairwise_count(word, section, sort = TRUE)

# we need to filter for at least relatively common words first
word_cors <- media_agency_section_words %>%
  group_by(word) %>%
  filter(n() >= 20) %>%
  pairwise_cor(word, section, sort = TRUE)

# Correlation of next word
# word_cors %>%
#   filter(item1 == "vaccine")

word_cors %>%
  filter(item1 %in% c("covid", "vaccine", "lockdown", "guateng")) %>%
  group_by(item1) %>%
  slice_max(correlation, n = 6) %>%
  ungroup() %>%
  mutate(item2 = reorder(item2, correlation)) %>%
  ggplot(aes(item2, correlation)) +
  geom_bar(stat = "identity") +
  facet_wrap(~ item1, scales = "free") +
  coord_flip()

set.seed(1234)
word_cors %>%
  filter(correlation > .7) %>%
  graph_from_data_frame() %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(edge_alpha = correlation), show.legend = FALSE, edge_width = 3) +
  geom_node_point(color = "lightblue", size = 5) +
  geom_node_text(aes(label = name), repel = TRUE) +
  theme_void()
```

```{r VADER}
vader_df <- vader_df(media_agency_df$text)
write_as_csv(vader_df, "data_in/vader")

vader_df <- read_csv("data_in/vader.csv")
vader_df <- vader_df %>% mutate("X1" = row_number())
media_agency_df <- media_agency_df %>% mutate("X1" = row_number())
media_vader_df <- media_agency_df %>% left_join(vader_df, by = "X1")

#sentiment
ggplotly(ggplot(data=media_vader_df, aes(x=created_at)) +
  geom_line(aes(y=rollmean(compound, k=30, na.pad = TRUE)), color="pink", size=.5)+
  geom_smooth(aes(y=compound)) +
  theme_minimal()+
  facet_wrap(~screen_name) +
  scale_y_continuous(expand = c(0,0)))


media_vader_df %>% filter(compound != 0)%>% mutate(created_at = as.Date(created_at)) %>%  group_by(created_at) %>% mutate(compound_daily = mean(compound), pos_daily = mean(pos), neg_daily = mean(neg), neu_daily = mean(neu))

#News sentiment increased over time, probably based on less restrictions and vaccines
media_vader_df %>% filter(compound != 0) %>% mutate(created_at = as.Date(created_at)) %>%  group_by(created_at) %>% mutate(compound_daily = mean(compound), pos_daily = mean(pos), neg_daily = mean(neg), neu_daily = mean(neu)) %>% 
  ggplot(aes(created_at)) +
  geom_line(aes(y = compound_daily, colour = "Compound")) +
  geom_smooth(aes(y = compound_daily), formula = y ~ x)

#sentiment
ggplot(data=media_vader_df, aes(x=created_at, fill = screen_name)) +
  stat_smooth(media_vader_df %>% filter(screen_name == "News24"), mapping =  aes(y = compound))+
  stat_smooth(media_vader_df %>% filter(screen_name == "dailymaverick"), mapping =  aes(y = compound))+
  stat_smooth(media_vader_df %>% filter(screen_name == "eNCA"), mapping =  aes(y = compound))+
  stat_smooth(media_vader_df %>% filter(screen_name == "SABCNews"), mapping =  aes(y = compound))+
  stat_smooth(media_vader_df %>% filter(screen_name == "TimesLIVE"), mapping =  aes(y = compound))+
  theme_minimal()+
  scale_y_continuous(expand = c(0,0), breaks = c(-0.1, 0)) 

ggplot(media_vader_df %>% arrange(compound) %>% mutate(X1 = factor(X1, levels = X1)), aes(x=X1)) +
  geom_point(mapping =  aes(y = compound)) +
  theme_minimal()+
  scale_y_continuous(expand = c(0,0)) 


```

```{r Interactions}
# media_vader_df %>% select(favorite_count, retweet_count) %>% mutate(favorite_count + retweet_count)

media_vader_df <- media_vader_df %>% 
  mutate(created_at = as.Date(created_at)) %>%  
  group_by(created_at) %>% 
  mutate(retweet_daily = mean(retweet_count), favorite_daily = mean(favorite_count), total = mean(favorite_count + retweet_count))
# mean for the day
ggplotly(ggplot(data = media_vader_df, aes(created_at)) +
  geom_ribbon(aes(ymin = 0, ymax = total, fill = "Total")) +
  geom_ribbon(aes(ymin = 0, ymax = favorite_daily, fill = "Favorites")) +
  geom_ribbon(aes(ymin = 0, ymax = retweet_daily, fill = "Retweets")) + 
    theme(
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks = element_blank(),

      legend.justification=c(0,0),
      legend.position=c(0,0),
      legend.background = element_blank(),
      legend.key = element_blank(),
      legend.title = element_blank(),

      plot.title = element_text(size=14, face="bold", margin = margin(0, 0, 10, 0), hjust = 0),
      plot.caption = element_text(face="bold", hjust = 0),
    )
  , tooltip = c("total", "favorite_daily", "retweet_daily", "created_at"))

#peak tweets
media_vader_df %>% 
  filter(created_at == "2021-05-01" ) %>% 
  slice_max(favorite_count + retweet_count) 
media_vader_df %>% 
  filter(created_at == "2021-05-23" ) %>% 
  slice_max(favorite_count + retweet_count) 

media_vader_df %>% 
   filter(screen_name == "dailymaverick") %>% 
  slice_max(favorite_count + retweet_count) 

class(media_vader_df$created_at)

# top tweets
# eNCA Call for ministers over 60 to resign https://t.co/wlEjJGEpuk
# SCORPIO\r\nFloyd Shivambu’s brother quietly pays back Rm admits he received the VBS money gratuitouslyLWeOmNWp
# TimesLIVE	Do you approve of Duduzane running for president? https://t.co/hCDVQGHRWy
# News24 Coca-Cola lost $4 billion in market value after Cristiano Ronaldo suggested people drink water instead | @BISouthAfrica
# SABCNews BREAKING NEWS: King of Eswatini has fled amid public violence in the country https://t.co/1jv8vVCw9d
```

```{r Topic Modelling}
tidy_matrix <- tidy_media_df %>% count(screen_name, word) %>% cast_dfm(screen_name, word, n)

media_lda <- LDA(tidy_matrix, k = 5, control = list(seed = 1234))

media_topics <- tidy(media_lda, matrix = "beta")

media_top_terms <- media_topics %>%
  group_by(topic) %>%
  slice_max(beta, n = 10) %>% 
  ungroup() %>%
  arrange(topic, -beta)

media_top_terms %>%
  mutate(term = reorder_within(term, beta, topic)) %>%
  pivot_wider(id_cols = term, names_from = topic, values_from = beta) %>%
  rename("Nations Address" = 2, "Getting vaccinated for third wave" = 3, "Announcement of vaccine" = 4, "Covid-19: South Africa update" = 5, "Vaccination distributions" = 6) %>%
  pivot_longer(cols = c(2,3,4,5,6), names_to = "topic", values_to = "beta") %>%
  drop_na() %>%
  ggplot(aes(beta, term, fill = factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  scale_y_reordered()
```

```{r Gap k justify}

install.packages("ldatuning")


install.packages("devtools")
devtools::install_github("nikita-moor/ldatuning")


library("ldatuning")


library("topicmodels")
data("AssociatedPress", package="topicmodels")
dtm <- AssociatedPress[1:10, ]


result <- FindTopicsNumber(
  tidy_matrix,
  topics = seq(from = 2, to = 15, by = 1),
  metrics = c("Griffiths2004", "CaoJuan2009", "Arun2010", "Deveaud2014"),
  method = "Gibbs",
  control = list(seed = 77),
  mc.cores = 2L,
  verbose = TRUE
)

FindTopicsNumber_plot(result)
```

